Exploring the Link Between Problem-Solving Strategies and Programming Performance: A Comparative Analysis of High and Low Performers

Abstract

Although studies have investigated how students’ problem-solving strategies influence their performance, few explore the comparison between high and low performers. Moreover, there is a lack of empirical evidence on the effect of students’ problem-solving strategies on their performance in visual programming systems. To address this research gap, we developed a visual programming system based on the self-regulated learning model, providing top-down and bottom-up perspectives. We recruited 35 university students to use the visual programming system and collected eye movement data to analyze their problem-solving strategies. Finally, 19 students’ data with the weighted gaze sampling rate exceeding 80% were used to ensure robust data reliability. The results revealed: (1) a positive correlation between performance and visits to the top-down tracking window, and a negative correlation with the bottom-up window; and (2) both high and low performers used both strategies, but high performers favored the top-down approach. The findings suggest that to better support low performers, the visual programming system should provide guidance on applying the top-down strategy for problem-solving. The implications of this study highlight the importance of problem-solving strategies in programming and suggest that incorporating visual scaffolding for the top-down approach may help low performers narrow the gap with high performers.

Keywords

eye tracking self-regulated learning programming performance problem solving strategy visual programming system

Get full access to this article

View all access options for this article.

References

Alkan

Cagiltay

(2007). Studying computer game learning experience through eye tracking. British Journal of Educational Technology, 38(3), 538–542. https://doi.org/10.1111/j.1467-8535.2007.00721.x

Arfé

Vardanega

Ronconi

(2020). The effects of coding on children’s planning and inhibition skills. Computers & Education, 148, 103807. https://doi.org/10.1016/j.compedu.2020.103807

Bell

Koulouri

Lauria

Macredie

R. D.

Sutton

(2014). Microblogging as a mechanism for human–robot interaction. Knowledge-Based Systems, 69, 64–77. https://doi.org/10.1016/j.knosys.2014.05.009

Bond

(2024). The international journal of educational technology in higher education: Content and authorship analysis 2010–2024. International Journal of Educational Technology in Higher Education, 21(1), 60. https://doi.org/10.1186/s41239-024-00492-z

Bozan

Taslidere

(2025). Effect of the 5E model enriched with coding and digital game design activities on gifted students’ academic achievement and problem-solving skills. Education and Information Technologies, 30(10), 13249–13280. https://doi.org/10.1007/s10639-024-13281-y

Brooks

(1983). Towards a theory of the comprehension of computer programs. International Journal of Man-Machine Studies, 18(6), 543–554. https://doi.org/10.1016/S0020-7373(83)80031-5

Carlisle

M. C.

Wilson

T. A.

Humphries

J. W.

Hadfield

S. M.

(2005). RAPTOR: A visual programming environment for teaching algorithmic problem solving. ACM SIGCSE Bulletin, 37(1), 176–180. https://doi.org/10.1145/1047124.1047411

Chao

P.-Y.

(2016). Exploring students’ computational practice, design and performance of problem-solving through a visual programming environment. Computers & Education, 95, 202–215. https://doi.org/10.1016/j.compedu.2016.01.010

Chen

Xiao

Chen

Song

Sun

(2024). ChatScratch: An AI-Augmented system toward autonomous visual programming learning for children aged 6-12. Proceedings of the 2024 CHI conference on human factors in computing systems (pp. 1–19). Association for Computing Machinery. https://doi.org/10.1145/3613904.3642229

10.

Chen

Z. H.

H. D.

Chou

C. Y.

(2019). Using game-based negotiation mechanism to enhance students’ goal setting and regulation. Computers & Education, 129, 71–81. https://doi.org/10.1016/j.compedu.2018.10.011

11.

Cheng

Zou

Xie

Wang

F. L.

(2024). Exploring differences in self-regulated learning strategy use between high-and low-performing students in introductory programming: An analysis of eye-tracking and retrospective think-aloud data from program comprehension. Computers & Education, 208, 104948. https://doi.org/10.1016/j.compedu.2023.104948

12.

Costa

J. M.

Miranda

G. L.

(2017). Relation between Alice software and programming learning: A systematic review of the literature and meta‐analysis. British Journal of Educational Technology, 48(6), 1464–1474. https://doi.org/10.1111/bjet.12496

13.

Curasma

R. P.

Huayta-Meza

F. T.

Jara

N. J.

Illesca-Cangalaya

O. F.

Porras-Ccancce

L. E.

Romero-Gabino

A. J.

Llamoca

R. Q.

(2025). Arduino and mBlock projects to enhance computational thinking in first-year engineering students. EBSCOhost. Mathematical Modelling of Engineering Problems, 12(1). https://openurl.ebsco.com/EPDB%3Agcd%3A9%3A13008521/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A182796604&crl=c&link_origin=scholar.google.com

14.

Dökme

İ.

Hancıoğlu

Z. Ş.

(2025). Three-stage robotic STEM program ignites secondary school students’ interest in STEM career and attitudes toward science. Education and Information Technologies, 30, 12079–12100. https://doi.org/10.1007/s10639-025-13318-w

15.

Dúo-Terrón

(2023). Analysis of scratch software in scientific production for 20 years: Programming in education to develop computational thinking and STEAM disciplines. Education Sciences, 13(4), 404. https://doi.org/10.3390/educsci13040404

16.

Eliza

Hakiki

Fadli

Ridoh

Fauziah

Suri

R. M.

Hermanto

Kurniawan

Hidayah

(2024). Android-based Mobile learning application using app inventor on computer operating system material: The development and validity study. TEM Journal, 13(1), 624–634. https://doi.org/10.18421/tem131-65

17.

Evans

M. A.

Saint-Aubin

(2005). What children are looking at during shared storybook reading: Evidence from eye movement monitoring. Psychological Science, 16(11), 913–920. https://doi.org/10.1111/j.1467-9280.2005.01636.x

18.

Fix

Wiedenbeck

(1996). An intelligent tool to aid students in learning second and subsequent programming languages. Computers & Education, 27(2), 71–83. https://doi.org/10.1016/0360-1315(96)00022-X

19.

Gandini

Lemaire

Dufau

(2008). Older and younger adults’ strategies in approximate quantification. Acta Psychologica, 129(1), 175–189. https://doi.org/10.1016/j.actpsy.2008.05.009

20.

Garcia

Falkner

Vivian

(2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123, 150–163. https://doi.org/10.1016/j.compedu.2018.05.006

21.

Gerosa

Koleszar

Tejera

Gómez-Sena

Carboni

(2021). Cognitive abilities and computational thinking at age 5: Evidence for associations to sequencing and symbolic number comparison. Computers and Education Open, 2, 100043. https://doi.org/10.1016/j.caeo.2021.100043

22.

Ghosh

Wronski

Koetsenruijter

Mueller

Wensing

(2023). Watching people decide: Decision prediction using heatmaps of reading of a decision-support document. Journal of Vision, 21(9), 2631. https://doi.org/10.1167/jov.21.9.2631

23.

Gong

Yang

H. H.

Cai

(2020). Exploring the key influencing factors on college students’ computational thinking skills through flipped-classroom instruction. International Journal of Educational Technology in Higher Education, 17(1), 19. https://doi.org/10.1186/s41239-020-00196-0

24.

Grebitus

Davis

G. C.

(2019). Does the new nutrition facts panel help compensate for low numeracy skills? An eye‐tracking analysis. Agricultural Economics, 50(3), 249–258. https://doi.org/10.1111/agec.12481

25.

Cheng

Miaoting

Poggio

Dong

(2025). Integrating project‐based learning with self‐regulated learning to enhance programming learning motivation. Journal of Computer Assisted Learning, 41(2), Article e70011. https://doi.org/10.1111/jcal.70011

26.

Gunbatar

M. S.

Karalar

(2018). Gender differences in middle school students’ attitudes and self-efficacy perceptions towards mBlock programming. European Journal of Educational Research, 7(4), 925–933. https://doi.org/10.12973/eu-jer.7.4.925

27.

Hessels

R. S.

Andersson

Hooge

I. T. C.

Nyström

Kemner

(2015). Consequences of eye color, positioning, and head movement for eye‐tracking data quality in infant research. Infancy, 20(6), 601–633. https://doi.org/10.1111/infa.12093

28.

Holding

D. H.

(1992). Theories of chess skill. Psychological Research, 54(1), 10–16. https://doi.org/10.1007/BF01359218

29.

Humble

Boustedt

Holmgren

Milutinovic

Seipel

Östberg

A. S.

(2023). Cheaters or AI-Enhanced learners: Consequences of ChatGPT for programming education. Electronic Journal of e-Learning, 22(2), 16–29. https://doi.org/10.34190/ejel.21.5.3154

30.

Hvelplund

(2014). Eye tracking and the translation process: Reflections on the analysis and interpretation of eye-tracking data. MonTi: Monografías de Traducción e Interpretación, 201–223. https://doi.org/10.6035/MonTI.2014.ne1.6

31.

Ifenthaler

(2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Journal of Educational Technology & Society, 15(1), 38–52. https://psycnet.apa.org/record/2012-24238-003

32.

Jian

Y.-C.

J.-H.

Hsiao

Y.-R.

(2019). Differentiated processing strategies for science reading among sixth-grade students: Exploration of eye movements using cluster analysis. Computers & Education, 142, 103652. https://doi.org/10.1016/j.compedu.2019.103652

33.

Justice

L. M.

Skibbe

Canning

Lankford

(2005). Pre‐schoolers, print and storybooks: An observational study using eye movement analysis. Journal of Research in Reading, 28(3), 229–243. https://doi.org/10.1111/j.1467-9817.2005.00267.x

34.

Kafai

Y. B.

Burke

(2013). The social turn in K-12 programming: Moving from computational thinking to computational participation. Proceeding of the 44th ACM technical symposium on computer science education (pp. 603–608). Association for Computing Machinery. https://doi.org/10.1145/2445196.2445373

35.

Kahn

Winters

(2021). Constructionism and AI: A history and possible futures. British Journal of Educational Technology, 52(3), 1130–1142. https://doi.org/10.1111/bjet.13088

36.

Kampylis

Dagienė

Bocconi

Chioccariello

Engelhardt

Stupurienė

Masiulionytė-Dagienė

Jasutė

Malagoli

Horvath

Jeffrey

(2023). Integrating computational thinking into primary and lower secondary education: A systematic review. Educational Technology & Society, 26(2), 99–117. https://doi.org/10.30191/ETS.202304_26(2).0008

37.

Kartal

Okyar

(2024). A bibliometric analysis of eye-tracking technology in second language research. Education and Information Technologies, 30(8), 9995–10021. https://doi.org/10.1007/s10639-024-13205-w

38.

Katona

(2022). Measuring cognition load using eye-tracking parameters based on algorithm description tools. Sensors, 22(3), 912. https://doi.org/10.3390/s22030912

39.

Liu

Sokolikj

Dahlstrom-Hakki

Israel

(2024). Using eye-tracking in education: Review of empirical research and technology. Educational Technology Research & Development, 72(3), 1383–1418. https://doi.org/10.1007/s11423-024-10342-4

40.

Kersten

Murphy

G. C.

(2006). Using task context to improve programmer productivity. Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering, 1–11. https://doi.org/10.1145/1181775.1181777

41.

Kim

Vasconcelos

Belland

B. R.

Umutlu

Gleasman

(2022). Debugging behaviors of early childhood teacher candidates with or without scaffolding. International Journal of Educational Technology in Higher Education, 19(1), 26. https://doi.org/10.1186/s41239-022-00319-9

42.

Kong

Lai

(2022). Computational identity and programming empowerment of students in computational thinking development. British Journal of Educational Technology, 53(3), 668–686. https://doi.org/10.1111/bjet.13175

43.

Kong

Lai

(2023). Effects of a teacher development program on teachers’ knowledge and collaborative engagement, and students’ achievement in computational thinking concepts. British Journal of Educational Technology, 54(2), 489–512. https://doi.org/10.1111/bjet.13256

44.

Kong

S.-C.

(2019). Components and methods of evaluating computational thinking for fostering creative problem-solvers in senior primary school education. In Computational thinking education (pp. 119–141). Springer Singapore. https://doi.org/10.1007/978-981-13-6528-7_8

45.

Lee

Nam

Han

Kim

H. P.

(2011). Micro interaction metrics for defect prediction. Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, 311–321. https://doi.org/10.1145/2025113.2025156

46.

Lindberg

R. S. N.

Laine

T. H.

Haaranen

(2019). Gamifying programming education in K‐12: A review of programming curricula in seven countries and programming games. British Journal of Educational Technology, 50(4), 1979–1995. https://doi.org/10.1111/bjet.12685

47.

Liu

Cui

(2025). Eye tracking technology for examining cognitive processes in education: A systematic review. Computers & Education, 229, 105263. https://doi.org/10.1016/j.compedu.2025.105263

48.

Mai

T. T.

Bezbradica

Crane

(2022). Learning behaviours data in programming education: Community analysis and outcome prediction with cleaned data. Future Generation Computer Systems, 127, 42–55. https://doi.org/10.1016/j.future.2021.08.026

49.

Mangaroska

Sharma

Gašević

Giannakos

(2022). Exploring students’ cognitive and affective states during problem solving through multimodal data: Lessons learned from a programming activity. Journal of Computer Assisted Learning, 38(1), 40–59. https://doi.org/10.1111/jcal.12590

50.

Mayer

R. E.

(1989). Models for understanding. Review of Educational Research, 59(1), 43. https://doi.org/10.2307/1170446

51.

Megino-Elvira

Martín-Lobo

Vergara-Moragues

(2016). Influence of eye movements, auditory perception, and phonemic awareness in the reading process. The Journal of Educational Research, 109(6), 567–573. https://doi.org/10.1080/00220671.2014.994197

52.

Milne

Rowe

(2002). Difficulties in learning and teaching programming—views of students and tutors. Education and Information Technologies, 7(1), 55–66. https://doi.org/10.1023/A:1015362608943

53.

Miltner

Nuñez

A. T.

Brendel

Chaudhuri

Dillig

(2022). Bottom-up synthesis of recursive functional programs using angelic execution. Proceedings of the ACM on Programming Languages, 6(POPL), 1-29. https://doi.org/10.48550/arXiv.2107.06253

54.

Molina

A. I.

Navarro

Ó.

Ortega

Lacruz

(2018). Evaluating multimedia learning materials in primary education using eye tracking. Computer Standards & Interfaces, 59, 45–60. https://doi.org/10.1016/j.csi.2018.02.004

55.

Obaidellah

Al Haek

Cheng

P. C.-H.

(2018). A survey on the usage of eye-tracking in computer programming. ACM Computing Surveys, 51(1), 5:1–58. https://doi.org/10.1145/3145904

56.

Papavlasopoulou

Sharma

Giannakos

M. N.

(2020). Coding activities for children: Coupling eye-tracking with qualitative data to investigate gender differences. Computers in Human Behavior, 105, 105939. https://doi.org/10.1016/j.chb.2019.03.003

57.

Pennington

(1987). Stimulus structures and mental representations in expert comprehension of computer programs. Cognitive Psychology, 19(3), 295–341. https://doi.org/10.1016/0010-0285(87)90007-7

58.

Pereira

F. D.

Oliveira

E. H. T.

Oliveira

D. B. F.

Cristea

A. I.

Carvalho

L. S. G.

Fonseca

S. C.

Toda

Isotani

(2020). Using learning analytics in the Amazonas: Understanding students’ behaviour in introductory programming. British Journal of Educational Technology, 51(4), 955–972. https://doi.org/10.1111/bjet.12953

59.

Qian

Lehman

(2018). Students’ misconceptions and other difficulties in introductory programming: A literature review. ACM Transactions on Computing Education, 18(1), 1–24. https://doi.org/10.1145/3077618

60.

Raschke

Blascheck

Burch

(2014). Visual analysis of eye tracking data. In Huang

(Ed.), Handbook of human centric visualization (pp. 391–409). Springer. https://doi.org/10.1007/978-1-4614-7485-2_15

61.

Roll

Baker

S. j. d. Aleven

Keodinger

R. (2014). On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments. Journal of the Learning Sciences, 23(4), 537–560. https://doi.org/10.1080/10508406.2014.883977

62.

Rosengrant

Hearrington

O’Brien

(2021). Investigating student sustained attention in a guided inquiry lecture course using an eye tracker. Educational Psychology Review, 33(1), 11–26. https://doi.org/10.1007/s10648-020-09540-2

63.

Rovshenov

Sarsar

(2023). Research trends in programming education: A systematic review of the articles published between 2012-2020. Journal of Educational Technology and Online Learning, 6(1), 48–81. https://doi.org/10.31681/jetol.1201010

64.

Saito

Yamaura

(2013). A new approach to programming language education for beginners with top-down learning. Proceedings of 2013 IEEE international conference on Teaching, Assessment and Learning for Engineering (TALE), Bali, Indonesia, 26–29 August 2013, pp. 752–755. https://doi.org/10.1109/TALE.2013.6654538

65.

Schophuizen

Kalz

(2020). Educational innovation projects in Dutch higher education: Bottom-up contextual coping to deal with organizational challenges. International Journal of Educational Technology in Higher Education, 17(1), 36. https://doi.org/10.1186/s41239-020-00197-z

66.

Schunk

D. H.

Zimmerman

B. J.

(Eds.), (1998). Self-regulated learning: From teaching to self-reflective practice. Guilford Press.

67.

Selcuk

N. A.

Kucuk

Sisman

(2024). Does really educational robotics improve secondary school students’ course motivation, achievement and attitude? Education and Information Technologies, 29(17), 23753–23780. https://doi.org/10.1007/s10639-024-12773-1

68.

Shaft

T. M.

Vessey

(1995). Research report—the relevance of application domain knowledge: The case of computer program comprehension. Information Systems Research, 6(3), 286–299. https://doi.org/10.1287/isre.6.3.286

69.

Shneiderman

Mayer

(1979). Syntactic/semantic interactions in programmer behavior: A model and experimental results. International Journal of Computer & Information Sciences, 8(3), 219–238. https://doi.org/10.1007/BF00977789

70.

Shute

V. J.

(1991). Who is likely to acquire programming skills? Journal of Educational Computing Research, 7(1), 1–24. https://doi.org/10.2190/VQJD-T1YD-5WVB-RYPJ

71.

Smith

D. C.

Cypher

Tesler

(2000). Programming by example: Novice programming comes of age. Communications of the ACM, 43(3), 75–81. https://doi.org/10.1145/330534.330544

72.

Soloway

Ehrlich

(1984). Empirical studies of programming knowledge. IEEE Transactions on Software Engineering, SE-10(5), 595–609. https://doi.org/10.1109/TSE.1984.5010283

73.

Song

Hong

E. Y.

(2021). Applying computational analysis of novice learners’ computer programming patterns to reveal self-regulated learning, computational thinking, and learning performance. Computers in Human Behavior, 120, 106746. https://doi.org/10.1016/j.chb.2021.106746

74.

Spohrer

J. C.

Soloway

(1986). Novice mistakes: Are the folk wisdoms correct? Communications of the ACM, 29(7), 624–632. https://doi.org/10.1145/6138.6145

75.

Stanton

Meyers

McCord

(2024). Interpreting the influence of heuristic and systematic cues on visual attention to food label claims. Journal of Applied Communications, 108(2), 4. https://doi.org/10.4148/1051-0834.2525

76.

Storey

M.-A.

(2006). Theories, tools and research methods in program comprehension: Past, present and future. Software Quality Journal, 14(3), 187–208. https://doi.org/10.1007/s11219-006-9216-4

77.

Stylianou

D. A.

Silver

E. A.

(2004). The role of visual representations in advanced mathematical problem solving: An examination of expert-novice similarities and differences. Mathematical Thinking and Learning, 6(4), 353–387. https://doi.org/10.1207/s15327833mtl0604_1

78.

A. Y. S.

Huang

C. S. J.

Yang

S. J. H.

Ding

T. J.

Hsieh

Y. Z.

(2015). Effects of annotations and homework on learning achievement: An empirical study of scratch programming pedagogy. Journal of Educational Technology & Society, 18(4), 331–343. https://doi.org/10.1111/j.1467-8535.2010.01126.x

79.

Taylor

MacNamara

Á.

Taylor

R. D.

(2022). Strategy in talent systems: Top-down and bottom-up approaches. Frontiers in Sports and Active Living, 4, 988631. https://doi.org/10.3389/fspor.2022.988631

80.

Tikva

Tambouris

(2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083

81.

Tsai

M.-J.

Hou

H.-T.

Lai

M.-L.

Liu

W.-Y.

Yang

F.-Y.

(2012). Visual attention for solving multiple-choice science problem: An eye-tracking analysis. Computers & Education, 58(1), 375–385. https://doi.org/10.1016/j.compedu.2011.07.012

82.

Tsai

M.-J.

Huang

L.-J.

Hou

H.-T.

Hsu

C.-Y.

Chiou

G.-L.

(2016). Visual behavior, flow and achievement in game-based learning. Computers & Education, 98, 115–129. https://doi.org/10.1016/j.compedu.2016.03.011

83.

Tsai

M.-J.

A.-H.

(2021). Visual search patterns, information selection strategies, and information anxiety for online information problem solving. Computers & Education, 172, 104236. https://doi.org/10.1016/j.compedu.2021.104236

84.

Tsarava

Moeller

Román-González

Golle

Leifheit

Butz

M. V.

Ninaus

(2022). A cognitive definition of computational thinking in primary education. Computers & Education, 179, 104425. https://doi.org/10.1016/j.compedu.2021.104425

85.

Türkoğlu

Yalçınalp

(2024). Investigating problem-solving behaviours of university students through an eye-tracking system using GeoGebra in geometry: A case study. Education and Information Technologies, 29, 15761–15791. https://doi.org/10.1007/s10639-024-12452-1

86.

van Renswoude

D. R.

Raijmakers

M. E.

Koornneef

Johnson

S. P.

Hunnius

Visser

(2018). Gazepath: An eye-tracking analysis tool that accounts for individual differences and data quality. Behavior Research Methods, 50(2), 834–852. https://doi.org/10.3758/s13428-017-0909-3

87.

Vieira

Gómez

R. L.

Gómez

Canu

Duque

(2023). Implementing unplugged CS and use-modify-create to develop student computational thinking skills: – A nationwide implementation in Colombia. Educational Technology & Society, 26(3), 155–175. https://doi.org/10.30191/ETS.202307_26(3).0012

88.

Voon

X. P.

Wong

S. L.

Wong

L. H.

Khambari

M. N. M.

Syed-Abdullah

S. I. S.

(2022). Developing computational thinking competencies through constructivist argumentation learning: A problem-solving perspective. International Journal of Information and Education Technology, 12(6), 529–539. https://doi.org/10.18178/ijiet.2022.12.6.1650

89.

Wang

T.-N.

Jian

Y.-C.

C.-J.

(2022). Science reading and self-regulated learning: Evidence from eye movements of middle-school readers. The Journal of Educational Research, 115(1), 11–24. https://doi.org/10.1080/00220671.2021.2012637

90.

Wass

S. V.

Forssman

Leppänen

(2014). Robustness and precision: How data quality may influence key dependent variables in infant eye‐tracker analyses. Infancy, 19(5), 427–460. https://doi.org/10.1111/infa.12055

91.

Wing

J. M.

(2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215

92.

Winslow

L. E.

(1996). Programming pedagogy—A psychological overview. SIGCSE Bull, 28(3), 17–22. https://doi.org/10.1145/234867.234872

93.

Yang

F.-Y.

Wang

H.-Y.

(2023). Tracking visual attention during learning of complex science concepts with augmented 3D visualizations. Computers & Education, 193, 104659. https://doi.org/10.1016/j.compedu.2022.104659

94.

Yang

Wang

Sun

Zheng

(2022). A reading model of young EFL learners regarding attention, cognitive-load and auditory-assistance. The Journal of Educational Research, 115(1), 51–63. https://doi.org/10.1080/00220671.2022.2027327

95.

Yang

Lai Cheng Lei

(2025). Multimodal processing in simultaneous interpreting with text: Evidence from ear-eye-voice span and performance. PLoS One, 20(7), Article e0326527. https://doi.org/10.1371/journal.pone.0326527

96.

Yukselturk

Altiok

(2017). An investigation of the effects of programming with scratch on the preservice IT teachers’ self‐efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801. https://doi.org/10.1111/bjet.12453

97.

Zimmerman

B. J.

(2000). Attaining self-regulation: A social cognitive perspective. In Handbook of self-regulation (pp. 13–39). New York: Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7

98.

Zimmerman

B. J.

(2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

99.

Zimmerman

B. J.

Schunk

D. H.

(2011). Self-regulated learning and performance: An introduction and an overview. In Handbook of self-regulation of learning and performance (1st ed., pp. 15–26). New York: Routledge. https://doi.org/10.4324/9780203839010.ch1

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.38 MB